Sentence examples for more powerful classifiers from inspiring English sources

Although GSTP1 and APC were univariately associated with death, they were not selected multivariately in the final models due to exclusion by the more powerful classifiers.

Our future works will focus on improving the prediction accuracy by developing more powerful classifiers and more accurate spatial clustering algorithms.

The CAD scheme could be further improved by considering more useful features and using a more powerful classifier.

In addition, in this situation training set could not be fully utilized to generating a more powerful strong classifier.

On this basis, the incorporation of more powerful signal peptide classifiers along with larger training data sets could markedly increase the performance of global subcellular predictors for the extracellular proteins of Gram-negative bacteria as well.

When our upright face detector was trained, we noticed the same phenomenon: most of the weak classifiers are located on the upper part of the face, and they are more powerful than the weak classifiers located on the lower part of the face.

Finally, the experimental results show that our proposed MMNN system has more powerful generalization capability than the classifiers of single 3-layered perceptron and modular neural networks adopting other task decomposition techniques, and has a less training time consumption.

However, if there is no censoring, and the purpose is to classify patients' survival risks at a specific time point of interest, then a binary classifier should be more powerful than a survival prediction model.

- If they disagree, this may indicate that the instance is difficult to predict reliably, then we use the second step with additions of a third classifier and a more powerful computational intelligence algorithm named Boosting with Bagging to break the tie (we will explain the Boosting with Bagging algorithm in a separate section later on).

DNN is a model with more powerful learning ability that could replace GMM when modeling cough classifiers.

Finally, a third goal is to test the relative power of the theoretically expected more powerful protocol ("Protocol II", that employs balanced repeated 10-fold cross-validation estimator with AUC as the error metric and SVMs as classifier).